Roles of dynamic state estimation in power system modeling, monitoring and operation

Power system dynamic state estimation (DSE) remains an active research area. This is driven by the absence of accurate models, the increasing availability of fast-sampled, time-synchronized measurements, and the advances in the capability, scalability, and affordability of computing and communications. This paper discusses the advantages of DSE as compared to static state estimation, and the implementation differences between the two, including the measurement configuration, modeling framework and support software features. The important roles of DSE are discussed from modeling, monitoring and operation aspects for today's synchronous machine dominated systems and the future power electronics-interfaced generation systems. Several examples are presented to demonstrate the benefits of DSE on enhancing the operational robustness and resilience of 21st century power system through time critical applications. Future research directions are identified and discussed, paving the way for developing the next generation of energy management systems and novel system monitoring, control and protection tools to achieve better reliability and resiliency.Departamento de Energía de EE. UU TPWRS-00771-202

On power system automation: a Digital Twin-centric framework for the next generation of energy management systems

The ubiquitous digital transformation also influences power system operation. Emerging real-time applications in information (IT) and operational technology (OT) provide new opportunities to address the increasingly demanding power system operation imposed by the progressing energy transition. This IT/OT convergence is epitomised by the novel Digital Twin (DT) concept. By integrating sensor data into analytical models and aligning the model states with the observed system, a power system DT can be created. As a result, a validated high-fidelity model is derived, which can be applied within the next generation of energy management systems (EMS) to support power system operation. By providing a consistent and maintainable data model, the modular DT-centric EMS proposed in this work addresses several key requirements of modern EMS architectures. It increases the situation awareness in the control room, enables the implementation of model maintenance routines, and facilitates automation approaches, while raising the confidence into operational decisions deduced from the validated model. This gain in trust contributes to the digital transformation and enables a higher degree of power system automation. By considering operational planning and power system operation processes, a direct link to practice is ensured. The feasibility of the concept is examined by numerical case studies.The electrical power system is in the process of an extensive transformation. Driven by the energy transition towards renewable energy resources, many conventional power plants in Germany have already been decommissioned or will be decommissioned within the next decade. Among other things, these changes lead to an increased utilisation of power transmission equipment, and an increasing number of complex dynamic phenomena. The resulting system operation closer to physical boundaries leads to an increased susceptibility to disturbances, and to a reduced time span to react to critical contingencies and perturbations. In consequence, the task to operate the power system will become increasingly demanding. As some reactions to disturbances may be required within timeframes that exceed human capabilities, these developments are intrinsic drivers to enable a higher degree of automation in power system operation. This thesis proposes a framework to create a modular Digital Twin-centric energy management system. It enables the provision of validated and trustworthy models built from knowledge about the power system derived from physical laws, and process data. As the interaction of information and operational technologies is combined in the concept of the Digital Twin, it can serve as a framework for future energy management systems including novel applications for power system monitoring and control, which consider power system dynamics. To provide a validated high-fidelity dynamic power system model, time-synchronised phasor measurements of high-resolution are applied for validation and parameter estimation. This increases the trust into the underlying power system model as well as the confidence into operational decisions derived from advanced analytic applications such as online dynamic security assessment. By providing an appropriate, consistent, and maintainable data model, the framework addresses several key requirements of modern energy management system architectures, while enabling the implementation of advanced automation routines and control approaches. Future energy management systems can provide an increased observability based on the proposed architecture, whereby the situational awareness of human operators in the control room can be improved. In further development stages, cognitive systems can be applied that are able to learn from the data provided, e.g., machine learning based analytical functions. Thus, the framework enables a higher degree of power system automation, as well as the deployment of assistance and decision support functions for power system operation pointing towards a higher degree of automation in power system operation. The framework represents a contribution to the digital transformation of power system operation and facilitates a successful energy transition. The feasibility of the concept is examined by case studies in form of numerical simulations to provide a proof of concept.Das elektrische Energiesystem befindet sich in einem umfangreichen Transformations-prozess. Durch die voranschreitende Energiewende und den zunehmenden Einsatz erneuerbarer Energieträger sind in Deutschland viele konventionelle Kraftwerke bereits stillgelegt worden oder werden in den nächsten Jahren stillgelegt. Diese Veränderungen führen unter anderem zu einer erhöhten Betriebsmittelauslastung sowie zu einer verringerten Systemträgheit und somit zu einer zunehmenden Anzahl komplexer dynamischer Phänomene im elektrischen Energiesystem. Der Betrieb des Systems näher an den physikalischen Grenzen führt des Weiteren zu einer erhöhten Störanfälligkeit und zu einer verkürzten Zeitspanne, um auf kritische Ereignisse und Störungen zu reagieren. Infolgedessen wird die Aufgabe, das Stromnetz zu betreiben anspruchsvoller. Insbesondere dort wo Reaktionszeiten erforderlich sind, welche die menschlichen Fähigkeiten übersteigen sind die zuvor genannten Veränderungen intrinsische Treiber hin zu einem höheren Automatisierungsgrad in der Netzbetriebs- und Systemführung. Aufkommende Echtzeitanwendungen in den Informations- und Betriebstechnologien und eine zunehmende Menge an hochauflösenden Sensordaten ermöglichen neue Ansätze für den Entwurf und den Betrieb von cyber-physikalischen Systemen. Ein vielversprechender Ansatz, der in jüngster Zeit in diesem Zusammenhang diskutiert wurde, ist das Konzept des so genannten Digitalen Zwillings. Da das Zusammenspiel von Informations- und Betriebstechnologien im Konzept des Digitalen Zwillings vereint wird, kann es als Grundlage für eine zukünftige Leitsystemarchitektur und neuartige Anwendungen der Leittechnik herangezogen werden. In der vorliegenden Arbeit wird ein Framework entwickelt, welches einen Digitalen Zwilling in einer neuartigen modularen Leitsystemarchitektur für die Aufgabe der Überwachung und Steuerung zukünftiger Energiesysteme zweckdienlich einsetzbar macht. In Ergänzung zu den bereits vorhandenen Funktionen moderner Netzführungssysteme unterstützt das Konzept die Abbildung der Netzdynamik auf Basis eines dynamischen Netzmodells. Um eine realitätsgetreue Abbildung der Netzdynamik zu ermöglichen, werden zeitsynchrone Raumzeigermessungen für die Modellvalidierung und Modellparameterschätzung herangezogen. Dies erhöht die Aussagekraft von Sicherheitsanalysen, sowie das Vertrauen in die Modelle mit denen operative Entscheidungen generiert werden. Durch die Bereitstellung eines validierten, konsistenten und wartbaren Datenmodells auf der Grundlage von physikalischen Gesetzmäßigkeiten und während des Betriebs gewonnener Prozessdaten, adressiert der vorgestellte Architekturentwurf mehrere Schlüsselan-forderungen an moderne Netzleitsysteme. So ermöglicht das Framework einen höheren Automatisierungsgrad des Stromnetzbetriebs sowie den Einsatz von Entscheidungs-unterstützungsfunktionen bis hin zu vertrauenswürdigen Assistenzsystemen auf Basis kognitiver Systeme. Diese Funktionen können die Betriebssicherheit erhöhen und stellen einen wichtigen Beitrag zur Umsetzung der digitalen Transformation des Stromnetzbetriebs, sowie zur erfolgreichen Umsetzung der Energiewende dar. Das vorgestellte Konzept wird auf der Grundlage numerischer Simulationen untersucht, wobei die grundsätzliche Machbarkeit anhand von Fallstudien nachgewiesen wird

Coordinated Voltage Control in Modern Distribution Systems

Modern distribution systems, especially with the presence of distributed generation (DG) and distribution automation are evolving as smart distribution systems. Distribution management systems (DMSs) with communication infrastructure and associated software and hardware developments are integral parts of the smart distribution systems. With such advancement in distribution systems, distribution system voltage and reactive power control are dominant by Volt/VAr (voltage and reactive power) optimisation and utilisation of DG for system Volt/VAr support. It is to be noted that the respective controls and optimisation formulations are typically adopted from primary, secondary and tertiary voltage and reactive power controls at upstream system level. However, the characteristics of modern distribution systems embedded with high penetration of DG are different from transmission systems and the former distribution systems with uni-directional power flow. Also, coordinated control of multiple Volt/VAr support DG units with other voltage control devices such as on-load tap changer (OLTC), line voltage regulators (VRs) and capacitor banks (CBs) is one of the challenging tasks. It is mainly because reverse power flow, caused predominantly by DG units, can influence the operation of conventional voltage control devices. Some of the adverse effects include control interactions, operational conflicts, voltage drop and rise cases at different buses in a network, and oscillatory transients. This research project aimed to carry out in-depth study on coordinated voltage control in modern MV distribution systems utilising DG for system Volt/VAr support. In the initial phase of the research project, an in-depth literature review is conducted and the specific research gaps are identified. The design considerations of the proposed coordinated voltage control, which also uses the concept of virtual time delay, are identified through comprehensive investigations. It emphasises on examining and analysing both steady-state and dynamic phenomena associated with the control interactions among multiple Volt/VAr support DG units and voltage control devices. It would be essential for ensuring effective coordinated voltage control in modern distribution systems. In this thesis, the interactions among multiple DG units and voltage control devices are identified using their simultaneous and non-simultaneous responses for voltage control through time domain simulations. For this task, an analytical technique is proposed and small signal modelling studies have also been conducted. The proposed methodology could be beneficial to distribution network planners and operators to ensure seamless network operation from voltage control perspective with increasing penetration of DG units. Notably, it has been found that the significant interactions among multiple DG units and voltage control devices are possible under conventional standalone, rule-based, and analytics based control strategies as well as with real-time optimal control under certain system conditions. In the second phase of the research project, the proposed coordinated voltage control strategy is elaborated. The control design considerations are fundamentally based on eliminating the adverse effects, which can distinctly be caused by the simultaneous and non-simultaneous responses of multiple Volt/VAr support DG units and voltage control devices. First, the concept of virtual time delay is applied for dynamically managing the control variables of Volt/VAr support DG units and voltage control devices through the proposed control parameter tuning algorithm. Because it has been found that the conventional time-graded operation cannot eliminate the adverse effects of DG-voltage control device interactions under certain system conditions. Secondly, the distinct control strategies are designed and tested for effectively and efficiently coordinating the operation of multiple Volt/VAr support DG units and voltage control devices in real-time. The test results have demonstrated that the proposed coordinated voltage control strategy for modern MV distribution systems can effectively be implemented in real-time using advanced substation centred DMS. The proposed coordinated voltage control strategy presented in this thesis may trigger paradigm shift in the context of voltage control in smart distribution systems. In the final phase of the research project, short-term and/or long-term oscillations which can be possible for a MV distribution system operation embedded with Volt/VAr support DG are discussed. Typically, the short-term oscillations are occurred due to interactions among different DG units and their controllers (i.e., inter-unit electro-mechanical oscillations in synchronous machine based DG units) while the long-term oscillations occurred due to DG-voltage control device interactions. Also, sustained oscillations may occur due to tap changer limit cycle phenomenon. The concept of alert-state voltage control is introduced for mitigating the sustained oscillations subjected to OLTC limit cycles in the presence of high penetration of DG. The investigative studies in this thesis further emphasise the requirements of supplementary control and other mitigating strategies for damping the oscillations in modern active MV distribution systems. The proposed research will pave the way for managing increasing penetration of DG units, with different types, technologies and operational modes, from distribution system voltage control perspective

Application of Deep Learning Methods in Monitoring and Optimization of Electric Power Systems

This PhD thesis thoroughly examines the utilization of deep learning techniques as a means to advance the algorithms employed in the monitoring and optimization of electric power systems. The first major contribution of this thesis involves the application of graph neural networks to enhance power system state estimation. The second key aspect of this thesis focuses on utilizing reinforcement learning for dynamic distribution network reconfiguration. The effectiveness of the proposed methods is affirmed through extensive experimentation and simulations.Comment: PhD thesi

Model-Free Methods to Analyze Pmu Data in Real-Time for Situational Awareness and Stability Monitoring

This dissertation presents and evaluates model-free methodologies to process Phasor Measurement Unit (PMU) data. Model-based PMU applications require knowledge of the system topology, most frequently the system admittance matrix. For large systems, the admittance matrix, or other system parameters, can be time-consuming to integrate into supporting PMU applications. These data sources are often sensitive and can require permissions to access, delaying the implementation of model-based approaches. This dissertation focuses on evaluating individual model-free applications to efficiently perform functions of interest to system operators for real-time situational awareness. Real-time situational awareness is evaluated with respect to central digitization where the PMU data is archived, and delays from telecommunication and system architecture are not considered. The PMU data available to utilities is often a subset of the overall system. Even without full observability, PMU data for observable portions of the system provides valuable, high-resolution information about the current system state. Methods are needed that can analyze and generate critical insight about the system in real-time to assist in detection and mitigation of major system events. All chapters address methodologies that can derive their output solely from the PMU signals. These methodologies are evaluated for their reliability and computational efficiency, considering a specific task of interest. Inter-area oscillations and poorly damped electromechanical modes are dangerous when undetected for extended periods of time, eventually leading to blackouts when unstable parameters are present. Prony Analysis and Matrix Pencil Method were selected in Chapter 4 for their proven effectiveness of estimating the dominant modes of an input signal; for purposes of this dissertation, the signal of interest for oscillation analysis is real power. The speed of convergence, accuracy of the methods, and viability when applied to utility PMU data were assessed to determine suitability to online system operation. Matrix Pencil Method was determined to provide more robust and computationally efficient estimation of key system modes for both simulated and real utility PMU data. The biorthogonal discrete wavelet transform, which can correlate frequency data to a time-domain solution, was utilized in Chapter 3 to create a methodology for event detection and classification for a subset of selected events. The derived methodology was shown to be effective for identification and classification of load and capacitor switch events, as well as breaker operation and faults. Methods to mimic the power flow Jacobian from discrete measurements are derived to assess system stability and eigenvalues in Chapter 2. These methods were effective for fast detection of unstable system parameters. Chapter 5, the most significant contribution of this dissertation, details derivations of a mathematical reduced system model and power flow Jacobian variants for more robust instability detection, system weak point identification, mitigation techniques, and state estimation capabilities. Considering the functions of all evaluated and developed model-free methodologies, event detection, event classification, detection of poorly damped oscillatory modes, and instability detection and mitigation can be achieved for situational awareness

Data-driven methods for real-time voltage stability assessment

Voltage instability is a phenomenon that limits the operation and the transmission capacity of a power system. An operation state close to the security limits enables a cost-effective utilization of the system but it could also make the system more vulnerable to disturbances. The transition towards a more sustainable energy system, with a growing share of renewable generation, will increase the complexity in voltage stability assessment and cause significant planning and operational challenges for transmission system operators.The overall aim of this thesis is to develop a real-time voltage stability assessment tool which can be used to assist transmission system operators in monitoring voltage security limits and to provide early warnings of possible voltage instability. The thesis first analyzes the difference between static and dynamic voltage security margins, both theoretically and numerically. The results of the analysis show that power systems with a high share of loads with fast restoration dynamics, such as induction motors or power electronic controlled loads, may cause conventional static methods to assess the voltage security margins to become unreliable. Methods relying on a dynamic assessment of the security margin are in these circumstances more reliable. However, dynamic assessment of voltage security margins is computationally challenging and can in most cases not be estimated in the time frame required by system operators in critical situations. To overcome this challenge, a machine learning-based method for fast and robust computing of the dynamic voltage security margin is proposed and tested in this thesis. The method, based on artificial neural networks, can provide real-time estimations of voltage security margins, which are then validated using a search algorithm and actual time-domain simulations. The two-step approach is proposed to mitigate any inconsistency issues associated with neural networks under new or unseen operating conditions. Finally, a new method for voltage instability prediction is developed. The method is proposed to be used as an online tool for system operators to predict the system’s near-future stability condition given the current operating state. The method uses a more advanced neural network based on long-short term memory. The results from case studies using the Nordic 32 test system show good performance and the network can accurately, within only a few seconds, predict voltage instability events in almost all test cases

Attributes of Big Data Analytics for Data-Driven Decision Making in Cyber-Physical Power Systems

Big data analytics is a virtually new term in power system terminology. This concept delves into the way a massive volume of data is acquired, processed, analyzed to extract insight from available data. In particular, big data analytics alludes to applications of artificial intelligence, machine learning techniques, data mining techniques, time-series forecasting methods. Decision-makers in power systems have been long plagued by incapability and weakness of classical methods in dealing with large-scale real practical cases due to the existence of thousands or millions of variables, being time-consuming, the requirement of a high computation burden, divergence of results, unjustifiable errors, and poor accuracy of the model. Big data analytics is an ongoing topic, which pinpoints how to extract insights from these large data sets. The extant article has enumerated the applications of big data analytics in future power systems through several layers from grid-scale to local-scale. Big data analytics has many applications in the areas of smart grid implementation, electricity markets, execution of collaborative operation schemes, enhancement of microgrid operation autonomy, management of electric vehicle operations in smart grids, active distribution network control, district hub system management, multi-agent energy systems, electricity theft detection, stability and security assessment by PMUs, and better exploitation of renewable energy sources. The employment of big data analytics entails some prerequisites, such as the proliferation of IoT-enabled devices, easily-accessible cloud space, blockchain, etc. This paper has comprehensively conducted an extensive review of the applications of big data analytics along with the prevailing challenges and solutions